Update README.md

README.md

@@ -1,3 +1,70 @@
+---
+dataset_name: "hlo-feature-dataset"
+pretty_name: "HLO Feature Dataset for Deep Learning Resource Estimation"
+dataset_type: "graph-and-tabular"
+license: "apache-2.0"
+task_categories:
+  - time-series-forecasting
+  - regression
+  - graph-machine-learning
+language: "en"
+tags:
+  - HPC
+  - resource-prediction
+  - XLA
+  - compiler-features
+  - deep-learning
+  - graph-learning
+  - scheduling
+size_categories:
+  - 1K<n<10K
+source_datasets:
+  - custom
+dataset_summary: >
+  The HLO Feature Dataset contains High-Level Optimizer (HLO) graph features and metadata extracted 
+  from deep learning training workloads. It is designed for tasks such as runtime prediction, resource 
+  estimation, and graph-based machine learning in HPC environments.
+
+  Each entry pairs model configuration metadata with compiler graph data stored in `.npz` format.
+  
+  Ideal for ML system optimization studies, GNN research, and AI workload scheduling.
+
+structured_data:
+  features:
+    - name: "batch"
+      type: "integer"
+    - name: "epochs"
+      type: "integer"
+    - name: "learn_rate"
+      type: "float"
+    - name: "gpu_core_count"
+      type: "integer"
+    - name: "gpu_memory_size"
+      type: "integer"
+    - name: "fit_time"
+      type: "float"
+    - name: "npz_path"
+      type: "string"
+  graph_data:
+    node_features: "node_feat"
+    edge_index: "edge_index"
+    additional_keys:
+      - "node_opcode"
+      - "node_config_ids"
+      - "node_splits"
+usage_example: |
+  ```python
+  from datasets import load_dataset
+  import numpy as np
+
+  dataset = load_dataset("your-username/hlo-feature-dataset")
+  sample = dataset['train'][0]
+
+  graph_data = np.load(sample['npz_path'])
+  node_features = graph_data['node_feat']
+  edges = graph_data['edge_index']
+
+---
 
 # HLO Feature Dataset for Deep Learning Resource Estimation
 
@@ -100,4 +167,4 @@ Specify your license here (e.g., MIT, Apache-2.0).
 ---
 
 ## Contributions
-Open to contributions! Feel free to suggest improvements or share your models trained on this dataset.
+Open to contributions! Feel free to suggest improvements or share your models trained on this dataset.